System Diagnosis Framework for Sustaining the Operational Fidelity of a Radar System

Kulevome, Delanyo Kwame Bensah; Wang, Hong; Wang, Xuegang; Cobbinah, Bernard M.; Agbley, Bless Lord Y.; Safder, Qurratulain

doi:10.1109/iccwamtip53232.2021.9674151

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…Its reliable operation is of utmost importance to ensure accurate target information. However, like any complex electronic system, radar receivers are prone to degradation over time due to various factors, including aging, harsh environmental conditions, and component wear out 2 . Timely detection and prediction of failures in radar receivers are crucial to minimize system downtime, prevent catastrophic failures, and reduce maintenance costs.…”

Section: Introductionmentioning

confidence: 99%

Systematic prognostics framework development approach for a radar receiver

Kulevome,

Wang,

Zhao

et al. 2024

J. Appl. Rem. Sens.

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Radar receivers are vital components in modern radar systems, and their reliable operation is crucial for accurate target detection and tracking. However, degrading receiver components can lead to reduced gain, increased noise levels, and decreased probability of detection affecting the overall radar performance. We present an efficient real-time prognostic framework for a radar receiver. The effect of the performance degradation of critical devices on the radar receiver is analyzed. A prognostic framework is developed based on the relationship between device health and receiver performance. Subsequently, an improved prognostic model based on the integration of Weibull distribution and long short-term memory network is developed and trained to accurately estimate the remaining useful life (RUL) of the receiver. Integrating survival analysis and deep learning techniques offers a robust solution for accurate RUL estimation, which can significantly enhance maintenance strategies. The proposed framework facilitates transitioning from traditional reactive maintenance practices to a predictive maintenance approach, thereby reducing downtime and improving the overall availability of radar receivers.

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Section: Introductionmentioning

confidence: 99%

Systematic prognostics framework development approach for a radar receiver

Kulevome,

Wang,

Zhao

et al. 2024

J. Appl. Rem. Sens.

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“…In practice, component inaccuracy and other operational factors influence system performance leading to an inevitable failure, which makes it deviate from its pristine value. 4 When array elements fail, the sidelobe level increases, impacting the array's ability to detect targets of interest. Additionally, it results in a considerable decrease in array gain due to the broadening of the main lobe.…”

Section: Introductionmentioning

confidence: 99%

“…As the array’s size increases, the probability of array element failure increases proportionately. In practice, component inaccuracy and other operational factors influence system performance leading to an inevitable failure, which makes it deviate from its pristine value 4 . When array elements fail, the sidelobe level increases, impacting the array’s ability to detect targets of interest.…”

Section: Introductionmentioning

confidence: 99%

Phased array antenna diagnosis from amplitude-only data using parallel deep learning models

Kulevome

Wang

et al. 2023

J. Appl. Rem. Sens.

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There is a need for a reliable diagnostic process that can provide the information required to maintain optimal operating conditions of phased array antennas. The phase and amplitude information can be retrieved from the complex radiated field. However, phase measurement is laborious and costly at high frequencies. To overcome this challenge, it is of great relevance to create diagnostics methods that solely utilize the amplitude data. The shape and characteristics of the radiated field of a phased array antenna are predicated on the number of operating elements in the array. This implies that a failed element in the array will change the nature of the radiated pattern. We can locate a failed element using a learning algorithm to map the location of a failed element to the radiated field. We propose a technique for finding failed elements using only amplitude data by dividing the array into multiple subarrays. Each subarray is trained with dedicated deep convolutional neural networks for faulty element identification. We evaluated the proposed approach using simulated data for 20 × 20 and 30 × 30 array structures. The results demonstrate that the proposed approach can effectively diagnose and locate failed elements in a phased array antenna using amplitude-only data.

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“…They are essential for modern military and civilian applications, including air traffic control, weather monitoring, and surveillance. However, these systems are complex and sophisticated, consisting of multiple devices and subsystems [2] that are susceptible to failure or degradation over time [3]. Physical deterioration, such as corrosion, mechanical wear, and thermal stress, can lead to performance degradation, including reduced range, accuracy, and reliability.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical PHM Framework for Phased Array Radar Systems

Kulevome,

Wang,

Zhao

et al. 2023

PIER M

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Phased array radar (PAR) systems are critical for modern defense and surveillance applications, but their reliability and availability are affected by various factors, including physical and performance degradation. Furthermore, implementing prognostics and health management (PHM) framework for the whole radar system is challenging. To address these issues, this paper proposes an efficient solution by hierarchically implementing PHM frameworks in an active PAR (APAR) system. The proposed framework subsumes device-level, subsystem-level, and system-level health prediction models to enable comprehensive health monitoring and maintenance decision-making. This approach addresses the unique challenges involved in implementing PHM for the APAR system and facilitates the transition from traditional reactive maintenance practices to a predictive maintenance approach, thereby improving the overall system. Mathematical models that relate the radar's physical degradation to its performance deterioration are formulated, analyzed, and presented. Subsequently, a Bayesian long short-term memory (BayesLSTM) architecture is developed and integrated into the proposed framework for estimating the remaining useful life (RUL) of critical devices/subsystems. The effectiveness of the proposed deep learning-based prognostic framework is evaluated through simulations and experimental studies. The proposed hierarchical framework has the potential to be applied to other radar systems that require effective health monitoring strategy.

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System Diagnosis Framework for Sustaining the Operational Fidelity of a Radar System

Cited by 3 publications

References 7 publications

Systematic prognostics framework development approach for a radar receiver

Systematic prognostics framework development approach for a radar receiver

Phased array antenna diagnosis from amplitude-only data using parallel deep learning models

A Hierarchical PHM Framework for Phased Array Radar Systems

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